ArticleScientific reports2019
DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
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Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Evaluating network-based algorithms for protein function prediction using plant protein-protein interaction networks.BMC plant biology · 2026Article
- Review
- ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases.Journal of imaging · 2025Article
- DeepES: deep learning-based enzyme screening to identify orphan enzyme genes.Bioinformatics (Oxford, England) · 2025Article
- pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.Scientific reports · 2025Article
- Multitask Learning-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM).Methods in molecular biology (Clifton, N.J.) · 2025Article
- ProT-GFDM: A generative fractional diffusion model for protein generation.Computational and structural biotechnology journal · 2025Article
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- A Benchmarking Platform for Assessing Protein Language Models on Function-Related Prediction Tasks.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Exploring protein natural diversity in environmental microbiomes with DeepMetagenome.Cell reports methods · 2024Article
- Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training.Briefings in bioinformatics · 2024Article
- PANDA-3D: protein function prediction based on AlphaFold models.NAR genomics and bioinformatics · 2024Article
- Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.BMC medical informatics and decision making · 2024Article
- Mutual annotation-based prediction of protein domain functions with Domain2GO.Protein science : a publication of the Protein Society · 2024Article
- A comprehensive review and comparison of existing computational methods for protein function prediction.Briefings in bioinformatics · 2024Review
- Improvements in viral gene annotation using large language models and soft alignments.BMC bioinformatics · 2024Article
- AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding.Genome biology · 2024Article
- Review
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5 authors.
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Abstract
Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required. Recently, deep learning based methods have outperformed conventional algorithms in computer vision and natural language processing due to the prevention of overfitting and efficient training. Here, we propose DEEPred, a hierarchical stack of multi-task feed-forward deep neural networks, as a solution to Gene Ontology (GO) based protein function prediction. DEEPred was optimized through rigorous hyper-parameter tests, and benchmarked using three types of protein descriptors, training datasets with varying sizes and GO terms form different levels. Furthermore, in order to explore how training with larger but potentially noisy data would change the performance, electronically made GO annotations were also included in the training process. The overall predictive performance of DEEPred was assessed using CAFA2 and CAFA3 challenge datasets, in comparison with the state-of-the-art protein function prediction methods. Finally, we evaluated selected novel annotations produced by DEEPred with a literature-based case study considering the 'biofilm formation process' in Pseudomonas aeruginosa. This study reports that deep learning algorithms have significant potential in protein function prediction; particularly when the source data is large. The neural network architecture of DEEPred can also be applied to the prediction of the other types of ontological associations. The source code and all datasets used in this study are available at: https://github.com/cansyl/DEEPred .
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